![]() ![]() Interestingly these numbers do not line up with which reports a 80/20 p圓/py2 split for self-reported data scientists (which being self reported may be wishful reporting) nor with which is ~69% (37885/17160) p圓 (which may represent 'power users' who are aware of conda-forge). There are no plans to remove old versions from pypi, you will be able to install Matplotlib 2.2.x on python 2.7, just as if you are using python 2.6 now and pip install matplotlib you will get 1.4, however to get new features you have to use newer versions of python. If there is someone who wants to take point on more extensive backports (non-critical bug fixes) and manage the 2.2.x release I am open to that, but so far no one has volunteered. We will be back porting critical bug fixes to 2.2.x and doing bug fixes for the 2.2 series until (see ). ![]() Master branch is currently p圓.5+ and the next feature release will be Matplotlib 3.0 this summer cut off the master branch. Should there be any correlation between download number and usage base, the step to drop python 2 support would cut the use base by Thank you for digging up and plotting that data! What can be concluded is that dropping python 2 support deprives a majority of downloaded python versions of the option to use the newest matplotlib. The percentage of downloads of python 2 compared to overall python downloads is slowly dropping within the last 2 years from over 80% to 70%, while the percentage of matplotlib downloads for python 2 compared to all matplotlib downloads is fluctuating between 50% and 70% with no clear tendency to be seen.ĭiscussion: Unfortunately those numbers do not provide an estimate of the actual usage of matplotlib or python. Those numbers correlate well with the download numbers for other packages from the scientific python universe, like numpy and pandas. Some 35% of downloads are for the various python 3 versions. A summary of this approach can be found in this blog post, from which the respective SQL queries are adapted for the case of interest here.Īnalysis: We observe that ~60% of matplotlib downloads during the month between 5th of march and the 5th of April are made up by the version for python 2.7. Method: The data is obtained from the the-psf:pypi.downloads dataset queried via the GoogleBigQuery API. Therefore find here a visualization of download numbers from pypi. Impressum.Discussion on such topic would require to be based on some more substantial grounds. * If you don’t rename the makefile, you must pass it’s name to make: make I have only tested it on my local machine yet. ![]() ![]() PYTHON =python $ -c "import scipy scipy.test('1', '10')" # Download all packages to this directory. ![]()
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